We propose Information Transmission as a novel perspective on the mobile Experience Sampling Method (ESM) to frame a research agenda with a sharpened focus on increasing data quality in ESM studies. In this view, good experience sampling transmits valid, relevant, and "noise-free" information from users' in-situ experiences to remote researchers. We identify key transmission channels, which motivate combinations of objective and subjective data (i.e. device sensors and machine learning, plus asking users). We discuss opportunities and challenges, and give examples from our previous and ongoing work on ESM tools.